Exploring forest changes in an Ips typographus L. outbreak area: insights from multi-temporal multispectral UAS remote sensing

Abstract

Uncrewed Aerial Systems (UAS) offer a versatile solution for monitoring forest ecosystems. This study aimed to develop and assess an individual tree-based methodology using multi-temporal, multispectral UAS images to track changes caused by the European spruce bark beetle (Ips typographus L.). The approach encompassed four key steps: (1) individual tree detection using structure-from-motion point clouds, (2) tree species classification, (3) health classification of spruce trees as healthy, declined, or dead, and (4) change detection, identifying fallen/removed trees and alterations in tree health status. The developed methodology was employed to quantify changes in a bark beetle outbreak area covering 215 hectares in southeastern Finland during 2019–2021. The dataset included two managed and two conserved forest areas. The uncertainty estimation demonstrated the overall accuracies ranging from 0.58 to 0.91 for individual tree detection, 0.84 for species classification, and 0.83–0.96 for health classification, and a F1-score of 0.91 for the fallen or removed tree detection. Maps and statistics were produced, containing information on the health of the spruce trees in the area and information on changes, including trees that died during monitoring and those that fell or were removed from the forest. The results demonstrated successful control of the outbreak in the managed stands, evidenced by moderate tree mortality. Conversely, in the conserved stands, the outbreak resulted in dramatic tree mortality. This method serves stakeholders by enabling large-scale outbreak impact monitoring, facilitating timely risk assessment, and validating bark beetle outbreak management strategies.